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Senior AI Research Scientist - Agentic AI

Nexus Future Labs
Austin
Estimated Salary
USD 160.000 – USD 220.000
New
Live Update
5 Juli 2026
Deadline
5 Jul 2027

Job Description

We are on a mission to define the technological landscape of 2026. Nexus Future Labs is seeking a visionary Senior AI Research Scientist to lead the development of next-generation Autonomous Agent architectures. You will be at the forefront of the AI revolution, building systems capable of complex reasoning, multi-step planning, and seamless human-machine collaboration.

In this role, you will not just apply existing models; you will pioneer the architecture that will power the intelligent applications of tomorrow. If you are passionate about the future of Artificial General Intelligence (AGI) and possess the technical depth to build robust, scalable systems, we want to hear from you.

Responsibilities

  • Lead Research: Design and implement novel Transformer architectures and reinforcement learning algorithms tailored for autonomous agent reasoning.
  • Model Optimization: Optimize large language models (LLMs) for low-latency inference in real-time decision-making environments.
  • Cross-Functional Collaboration: Partner with product engineering teams to integrate cutting-edge AI research into scalable production applications.
  • Technical Leadership: Mentor junior researchers and contribute to the scientific discourse on AI safety and alignment.
  • Patent Development: Document and file patents for novel algorithms and system architectures.

Qualifications

  • Education: PhD or Master’s degree in Computer Science, Mathematics, or a related technical field with a focus on AI/ML.
  • Experience: 5+ years of professional experience in Machine Learning, Deep Learning, or Natural Language Processing.
  • Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and distributed training frameworks.
  • Language Model Expertise: Deep understanding of Transformer models, attention mechanisms, and fine-tuning strategies.
  • Problem Solving: Demonstrated ability to tackle complex, ambiguous problems in high-performance computing environments.

Required Skills

Python PyTorch TensorFlow Large Language Models Machine Learning Deep Learning NLP Reinforcement Learning CUDA Distributed Systems

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